Steganography is a technique to embed message into media content in a human imperceptible way. A common way to add steganographic signal to image is to add it into the frequency domain. The spatially described image data is transformed to frequency representation using for example Fourier Transform. As human vision is not sensitive to changes in higher frequency image features, this method shows high imperceptibility. Besides this, the yellow-blue color channel can be used to embed message, as human vision is not sensitive to change in this channel.
Image recognition is a computer vision technology to extract optical information from an image to make decision. Some common algorithms exist to extract image features such as Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Feature (SURF). These algorithms create feature descriptors from the input image frame. Using these descriptors, a system can perform matching process to check the relevance of the product to the product database. Another common application of computer vision technology is Optical Character Recognition (OCR). By matching objects in the image frame with font templates, a system can extract characters as well as entire text strings. This further enhances the efficiency of database lookup for the matching product entries.
The information that can be stored within a hidden image is limited, for example, 20 bit which is 1 million variations. Therefore it is desirable to increase the amount of information and variations that can be stored in a hidden image and maintain performance of a system for retrieving associated information using an image.